Negative ion saliva marker related to brain glioma and meningioma and application of negative ion saliva marker

Abstract

The invention discloses a negative ion saliva marker related to brain glioma and meningioma and application of the negative ion saliva marker, and relates to the field of biological medicine. The negative ion saliva marker comprises 2-(4-chloro-2-methyl phenoxy) acetic acid and / or quillaic acid. The invention provides a kit, which is used for detecting negative ion saliva markers related to brain glioma and meningioma and comprises a specific detection reagent. Besides, the invention further provides a computer program product which can judge whether the patient to be detected is a glioma patient or a meningioma patient, so that a solid foundation is laid for subsequent treatment. The method has good feasibility and accuracy, is safe and noninvasive, can effectively distinguish brain glioma and meningioma, and provides a brand new powerful tool for clinical diagnosis.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biological medicine, and particularly relates to a negative ion saliva marker related to brain glioma and meningioma and application thereof. BACKGROUND

[0002] Brain glioma and meningioma both belong to subtypes of intracranial tumors, but there is a significant difference in the harmfulness of brain glioma and meningioma to the human body. Brain glioma is a tumor originating from brain neuroglia cells, which is mostly malignant and highly invasive. Meningioma is a tumor originating from the meninges (membrane covering the surface of the brain and spinal cord), which is mostly benign and has clear boundaries.

[0003] In current clinical practice, imaging detection is the core detection means for brain glioma and meningioma (such as computed tomography CT and magnetic resonance imaging MRI), which has the following significant limitations in clinical application: 1) Imaging examination has insufficient sensitivity to early or small lesions, which easily leads to missed diagnosis or delayed diagnosis; 2) Low-grade brain glioma (such as grade II astrocytoma) may appear as a lesion with a relatively clear boundary and no enhancement, especially in the case of meningioma combined with cystic degeneration or calcification; 3) Atypical meningioma (WHO grade II) can appear infiltrative growth, uneven enhancement and peripheral edema, which is similar to high-grade glioma (such as anaplastic astrocytoma) in imaging performance.

[0004] In the case that imaging detection cannot distinguish brain glioma and meningioma, people often distinguish brain glioma and meningioma through invasive biopsy (i.e. pathological biopsy), but invasive biopsy, although being the gold standard for diagnosis, has the risks of surgical trauma, nerve function damage and sampling error.

[0005] Therefore, in the existing methods for distinguishing brain glioma and meningioma, imaging detection has the problem that the accuracy of the detection result needs to be further improved, and invasive biopsy (i.e. pathological biopsy) has the problem that there is a risk of surgical trauma (even brain nerve function damage) during detection, so it is necessary to improve it. SUMMARY

[0006] In view of the above technical problems, the present application provides a negative ion saliva marker related to brain glioma and meningioma and application thereof based on the negative ion mode in mass spectrometry (MS), so as to provide a new idea and approach for distinguishing brain glioma and meningioma.

[0007] The technical solution provided by the present application is as follows: In a first aspect, a negative-ion saliva marker associated with intracranial tumors is provided, characterized in that the negative-ion saliva marker comprises 2-(4-chloro-2-methylphenoxy)acetic acid and / or Quillaic acid.

[0008] In the above technical solution, the negative-ion saliva marker further comprises one or more of Anthraflavic acid, Ciprofibrate, Ganoderic acid A, Lapachol, and Mesoporphyrin IX.

[0009] In the above technical solution, the negative-ion saliva marker comprises 2-(4-chloro-2-methylphenoxy)acetic acid, Quillaic acid, Anthraflavic acid, Ciprofibrate, Ganoderic acid A, Lapachol, and Mesoporphyrin IX.

[0010] In a second aspect, a use of a reagent for detecting the negative-ion saliva marker of the first aspect in the preparation of a product for diagnosing brain glioma is provided.

[0011] In a third aspect, a kit is provided, which contains a reagent for detecting the negative-ion saliva marker of the first aspect.

[0012] In a fourth aspect, a use of the kit of the third aspect in the preparation of a product for brain glioma is provided.

[0013] In a fifth aspect, a use of the negative-ion saliva marker in the kit of the fourth aspect in non-diagnostic brain glioma is provided.

[0014] In a sixth aspect, a product for diagnosing brain glioma is provided, which comprises primers, probes, antibodies, aptamers, or chips specific to the negative-ion saliva marker of the first aspect.

[0015] In a seventh aspect, a computer program product is provided, which is used to determine whether a patient to be tested is a brain glioma patient or a meningioma patient, and comprises the following steps: obtaining the expression amount of a single negative-ion saliva marker of the patient to be tested; The expression amount of each single negative ion saliva marker is substituted into a binary logistic regression equation to calculate the log of the advantage y of the patient to be tested, the negative ion saliva markers including 2-(4-chloro-2-methylphenoxy) acetic acid, Quillaic acid, Anthraflavic acid, Ciprofibrate, Ganoderic acid A, Lapachol, and Mesoporphyrin IX; According to y, the probability Z that the patient to be tested is a brain glioma patient is calculated, Z = exp (y) / {1 + exp (y)}; wherein, exp (y) is the exponential function of y; According to the comparison of the probability Z and the reference value, it is judged which one of the brain glioma patient and the meningioma patient the patient to be tested is.

[0016] In the above technical solution, the formula of the binary logistic regression equation is: y = A + B1 x x1 + B2 x x2 + B3 x x3 + B4 x x4 + B5 x x5 + B6 x x6 + B7 x x7; Wherein, A is the intercept term, B1-B7 are the regression coefficients of the independent variables; x1 is the expression amount of 2-(4-chloro-2-methylphenoxy) acetic acid; x2 is the expression amount of Anthraflavic acid; x3 is the expression amount of Ciprofibrate; x4 is the expression amount of Ganoderic acid A; x5 is the expression amount of Lapachol; x6 is the expression amount of Mesoporphyrin IX; and x7 is the expression amount of Quillaic acid.

[0017] In the above technical solution, A is -3.0954353173525, B1 is -4.22561095659038 10 -10 , B2 is -9.00482035672691 10 -9 , B3 is 6.29522397116953 10 -9 , B4 is 2.05175662876307 10 -8B5 is 2.36382458451883 10 -8 B3 is -4.34068596792601 10 -9 B3 is 5.03887612874253 10 -9 .

[0018] It should be noted that the patients to be tested in this invention are those who have been preliminarily diagnosed by medical staff as having glioma or meningioma after imaging examinations. This invention is intended to help medical staff further distinguish and confirm whether the patients to be tested have glioma or meningioma, especially when the patients are unwilling to undergo invasive biopsy (invasive biopsy carries the risk of surgical trauma and neurological damage).

[0019] The beneficial effects of this invention are as follows: 1. This invention provides a negative ion salivary biomarker related to gliomas and meningiomas, and its application. It allows for the screening of acidic (or slightly acidic) compounds associated with gliomas and meningiomas from the saliva of patients to help medical personnel distinguish between glioma and meningioma. This method is feasible, accurate, safe, and non-invasive. Medical personnel can use one or more negative ion salivary biomarkers discovered in this invention to detect and diagnose patients' saliva, determining whether the patient has a glioma or meningioma. This invention can serve as a new approach and method for differentiating between gliomas and meningiomas, providing a powerful new tool for clinical diagnosis.

[0020] 2. Based on the negative ion mode in mass spectrometry (MS), this invention has discovered seven acidic (or slightly acidic) compounds that can be detected in patient saliva and are associated with gliomas and meningiomas. These include 2-(4-chloro-2-methylphenoxy)acetic acid, quiillaicacid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX. These seven negative ion compounds have high specificity and sensitivity as detection variables. They can be used as detection markers to diagnose whether a patient has a glioma or a meningioma.

[0021] 3. Further research revealed that among the seven negative ion compounds mentioned above, seven compounds were present in higher concentrations in patients with meningiomas, including: 2-(4-chloro-2-methylphenoxy)acetic acid, quillic acid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX.

[0022] 4. This invention also provides a reagent and kit that can use the aforementioned seven negative ion compounds as detection markers to distinguish between glioma and meningioma in a patient being tested. This method is completely non-invasive and highly accurate. Furthermore, the seven negative ion salivary markers can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0023] 5. This invention also provides a product for distinguishing between gliomas and meningiomas. This product can calculate the probability that a patient has a glioma based on the expression levels of various acidic compounds, and then compare this probability with reference values, thereby helping medical personnel determine whether the patient has a glioma or a meningioma. This product has good feasibility and accuracy, and can effectively distinguish between glioma and meningioma patients, providing a new tool and approach for clinical diagnosis. Attached Figure Description Figure 1 The graph shows the fold change analysis results of the expression levels of markers showing significant differences in negative ion patterns. Figure 2 Box plot of significance for negative ion mode difference markers; Figure 3 This is the ROC diagnostic curve. Detailed Implementation

[0024] Mass spectrometry (MS) is a highly sensitive analytical technique that identifies the structure and composition of compounds by measuring the mass-to-charge ratio (m / z) of ions. Its core is to convert sample molecules into gaseous ions and then separate and detect them based on their mass differences.

[0025] Positive ion mode and negative ion mode are two core and selectable detection methods in mass spectrometry (MS). The detection mode (positive ion mode or negative ion mode) selected by the operator directly affects the sensitivity and accuracy of the analysis results.

[0026] The similarities between the positive ion mode and the negative ion mode include: (1) The sample pretreatment steps (such as extraction, centrifugation, and filtration of saliva samples) are usually the same in both positive and negative ion modes. (2) The liquid chromatography (LC) separation conditions (such as column, mobile phase, and gradient) used in both modes are usually the same. (3) Both modes acquire data by mass spectrometry and generate mass spectra and chromatograms for subsequent analysis. (4) The ultimate goal of both modes is to detect and identify compounds in the sample as comprehensively and accurately as possible.

[0027] The differences between the positive ion mode and the negative ion mode mentioned above include: (1) different ionization methods. (2) different types of compounds to be detected (positive ion mode is more suitable for detecting basic compounds (such as amino acids, amines, and some lipids); negative ion mode is more suitable for detecting acidic compounds (such as organic acids, phenols, and some lipids); (3) different sensitivity and response: some compounds respond more strongly in positive ion mode, while others respond more strongly in negative ion mode. For example, lipids are usually easier to detect in positive ion mode, while organic acids are easier to detect in negative ion mode. (4) different background noise and interference: positive ion mode may be more susceptible to matrix effects (such as salts and solvent impurities); negative ion mode may be more susceptible to interference from carbon dioxide in the air and acidic impurities in the solvent.

[0028] Positive ion compounds carry a positive charge, and positive ion mode performs analysis by detecting positively charged ions; negative ion compounds carry a negative charge, and negative ion mode performs analysis by detecting negatively charged ions. The main differences between positive ion mode and negative ion mode are as follows: 1. Because basic or slightly alkaline compounds ionize more easily in positive ion mode, positive ion mode is more suitable for detecting basic or slightly alkaline compounds, such as amino acids (e.g., leucine, lysine), amines (e.g., choline, histamine), and some lipids (e.g., phosphatidylcholine, triglycerides). 2. Because acidic or slightly acidic compounds are more easily ionized in negative ion mode, negative ion mode is more suitable for detecting acidic or slightly acidic compounds, such as organic acids (e.g., citric acid, succinic acid), phenols (e.g., caffeic acid, ferulic acid), and some lipids (e.g., fatty acids, phosphatidylserine).

[0029] Currently, the main methods used to diagnose gliomas and meningiomas are categorized as follows: computed tomography (CT), magnetic resonance imaging (MRI), and molecular subtyping. The diagnosis of gliomas and meningiomas has entered the molecular era, but widespread adoption of the technology, management of heterogeneity, and clinical translation remain bottlenecks that urgently need to be overcome, as explained below: CT scans work by utilizing the differences in X-ray absorption rates among different tissue densities in the body. After analog-to-digital conversion by a computer, images with varying grayscale levels are created. Gliomas, due to their tumor components, surrounding edema, cystic degeneration, or hemorrhage, produce corresponding density differences, thus displaying images with varying grayscale levels from black to white. Diagnostic physicians can use this to determine the nature of the lesion. CT enhancement techniques involving intravenous injection of high-density contrast agents are also commonly used in glioma diagnosis. After the contrast agent enters the bloodstream, different enhancement patterns can appear due to the degree of damage the tumor causes to the blood-brain barrier and the difference in blood supply between the tumor and surrounding normal brain tissue. Compared to plain CT scans without contrast agents, its diagnostic efficiency is greatly improved.

[0030] The advent of magnetic resonance imaging (MRI) technology has been epoch-making in the imaging diagnosis of brain tumors, and its value in glioma diagnosis is widely recognized and applied. Due to the different imaging principles, MRI has a significantly higher resolution for normal and pathological tissues than CT, and it also has the advantage of no radiation damage. Low-grade gliomas often do not have a significant density difference with normal brain tissue, and the blood-brain barrier is not significantly disrupted; therefore, plain CT and enhanced CT are not accurate enough in their diagnosis. MRI scans not only have higher tissue resolution, but their multi-sequence examination using different technical principles can also provide a rough assessment of tissue composition. Unlike CT's simple transverse tomographic scan, MRI can obtain more anatomical information by cutting at any angle. Even in terms of plain scans (without contrast agents), MRI's overall tumor assessment efficiency surpasses that of CT.

[0031] Molecular subtyping diagnosis is a method based on molecular biology and bioinformatics technologies that classifies and diagnoses diseases by analyzing the molecular characteristics of samples (such as genomics, transcriptomics, and proteomics data). It shifts the basis of disease classification from traditional pathological features to molecular characteristics, enabling a more precise analysis of disease heterogeneity and thus providing a basis for personalized diagnosis. While molecular subtyping diagnosis is considered the "gold standard" for diagnosing gliomas, it requires surgical or biopsy procedures to obtain pathological tissue, posing a risk of trauma. Furthermore, gene sequencing takes time, potentially delaying diagnostic decisions. Additionally, gliomas exhibit spatial heterogeneity, and a single biopsy may miss key molecular markers, leading to inaccurate subtyping in glioma diagnosis.

[0032] Therefore, current methods for diagnosing and differentiating between gliomas and meningiomas cannot simultaneously satisfy the requirements of being safe, non-invasive, and having high detection accuracy.

[0033] To address the aforementioned issues, this invention, based on the negative ion mode in mass spectrometry (MS), discovered seven acidic (or slightly acidic) compounds detectable in patient saliva and associated with gliomas and meningiomas. These seven acidic (or slightly acidic) compounds can assist medical personnel in further diagnosing and differentiating between gliomas and meningiomas, demonstrating good feasibility, accuracy, and complete non-invasiveness. Furthermore, to make the technical solution of this invention clearer and easier to understand, the molecular formulas and structural formulas of the seven negative ion salivary biomarkers involved in the embodiments are described below: 2-(4-Chloro-2-methylphenoxy)acetic acid: Molecular formula: C9H9ClO3 Structural formula:

[0034] Anthraflavic acid molecular formula: C 14 H8O4 Structural formula:

[0035] Ciprofibrate molecular formula: C 13 H 14 Cl2O3 Structural formula:

[0036] Ganoderic acid A (C60) 30 H 44 O7 Structural formula:

[0037] Lapachol molecular formula: C 15 H 14 O3 Structural formula:

[0038] Mesoporphyrin IX molecular formula: C 34 H 38 N4O4 Structural formula:

[0039] Quilaic acid molecular formula: C30 H 46 O5 Structural formula:

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0041] In practical work, in order to evaluate whether salivary compounds detected in patients' saliva can serve as a diagnostic factor for distinguishing between glioma and meningioma, this invention collects samples from glioma and meningioma patients, performs metabolomic sequencing, and uses bioinformatics to statistically analyze the sequencing data. It discovers positively ionized acidic compounds associated with glioma and meningioma, integrates the compounds with disease information, and predicts and distinguishes between glioma and meningioma patients to the greatest extent possible.

[0042] This invention primarily relates to a sialic acid biomarker, product, and its application for differentiating between gliomas and meningiomas. The general idea of ​​the overall solution is as follows: for patients with gliomas and meningiomas: ... Figure 1 As shown, we conducted experimental tests on saliva samples from patients with gliomas and meningiomas (specific experimental methods are described in Examples 1-3). Seven compounds were found to be highly correlated with gliomas and meningiomas. Based on this, we screened out seven negative ion compounds using a pre-designed experimental method and input the relevant quantitative data (expression level) of these compounds into a binary logistic regression equation to obtain the logarithm y of the patient's odds. Then, we calculated the probability Z of the patient having a glioma using the formula Z=exp(y) / {1+exp(y)}. The accuracy of this detection method exceeded 99%.

[0043] Example 1: Sample Collection Saliva samples were collected from 115 patients with gliomas and 95 patients with meningiomas: The sample source and inclusion criteria for the glioma group are as follows: recruited from Zhongnan Hospital of Wuhan University. Inclusion criteria: 1. Age greater than 18 years; 2. Diagnosed with glioma (confirmed by imaging, histopathology, or molecular pathology); 3. Not receiving antibiotic or immunosuppressive therapy within one month prior to biological sample collection; 4. The patient or their guardian agrees to participate in the study and signs an informed consent form.

[0044] Exclusion criteria for glioma group: 1. Comorbid other malignant tumors; 2. Comorbid severe oral diseases or oral treatment within the past month; 3. Severe liver disease and kidney damage or receiving continuous renal replacement therapy, hemodialysis or peritoneal dialysis; 4. Unable to cooperate in completing saliva sample collection.

[0045] The sample sources and inclusion criteria for the meningioma group are as follows: recruited from Zhongnan Hospital of Wuhan University. Inclusion criteria: 1. Age greater than 18 years; 2. Diagnosed with meningioma (imaging or histopathological diagnosis); 3. Not having received antibiotic or immunosuppressant treatment within 1 month prior to sampling; 4. The patient or their guardian agrees to participate in the study and signs an informed consent form.

[0046] Exclusion criteria for meningioma: 1. Comorbid malignant tumors; 2. Comorbid severe oral diseases or oral treatment within the past month; 3. Severe systemic diseases and active infections; 4. Inability to cooperate in completing saliva sample collection.

[0047] Table 1 Sample Information Table

[0048] Example 2: Sample Extraction Saliva samples were collected from the patients according to the standard technique described by Navazesh (1993). Thirty minutes before saliva collection, the subjects thoroughly rinsed their mouths with purified water. The subjects were seated comfortably with their eyes open and head slightly tilted forward, resting for 5 minutes and minimizing facial movements. Saliva was collected at the bottom of the mouth and spat into a collection tube every 60 seconds. The mixture was thoroughly mixed; if the sample was clear, it was collected directly. If turbid, it was centrifuged at 3000g for 10 minutes at 4°C. The supernatant was collected, and approximately 100µl was aliquoted into sterile centrifuge tubes for metabolomics analysis. The samples were then flash-frozen in liquid nitrogen for 5-10 minutes and stored at -80°C for analysis.

[0049] Example 3: Data Statistics and Analysis 3.1 Experimental Methods S1: After the sample is slowly thawed at 4℃, an appropriate amount of sample is added to a pre-cooled methanol / acetonitrile / water solution (2:2:1, v / v), vortexed, sonicated at low temperature for 30 min, allowed to stand at -20℃ for 10 min, centrifuged at 14000 g at 4℃ for 20 min, the supernatant is taken and vacuum dried, and 100 μL of acetonitrile water solution (acetonitrile:water = 1:1, v / v) is added to redissolve the sample for mass spectrometry analysis, vortexed, centrifuged at 14000 g at 4℃ for 15 min, and the supernatant is injected for analysis.

[0050] S2: Samples were separated using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system with a HILIC column; column temperature 25℃; flow rate 0.3 mL / min; injection volume 2 μL; mobile phase composition A: water + 25 mM ammonium acetate + 25 mM ammonia, B: acetonitrile; gradient elution program as follows: 0-1.5 min, 98% B; 1.5-12 min, B linearly changes from 98% to 2%; 12-14 min, B maintains at 2%; 14-14.1 min, B linearly changes from 2% to 98%; 14.1-17 min, B maintains at 98%; throughout the analysis, samples were placed in an autosampler at 4℃. To avoid the influence of instrument signal fluctuations, samples were analyzed continuously in a randomized order. QC (Quality Control) samples were inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of experimental data. The QC sample is an equal mixture of all the samples being tested. The consistency of the QC sample is used to determine the stability of the instrument in sample testing.

[0051] S3: Primary and secondary spectra of the samples were acquired using a Q Exactive series mass spectrometer. After separation using a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system, the samples were analyzed by mass spectrometry using a Q Exactive series mass spectrometer (Thermo). Electrospray ionization (ESI) was employed in negative ion mode for detection. The ESI source and mass spectrometer settings were as follows: Auxiliary heating gas 1 (Gas1): 60, Auxiliary heating gas 2 (Gas2): 60, Curtain gas (CUR): 30 psi, Ion source temperature: 600℃, Spray voltage (ISVF): ±5500 V (both positive and negative modes); Primary mass-to-charge ratio detection range: 80-1200 Da, resolution: 60000, cumulative scan time: 100 ms; Secondary mass-to-charge ratio was acquired using a segmented acquisition method, with a scan range of 70-1200 Da, secondary resolution: 30000, cumulative scan time: 50 ms, dynamic exclusion time: 4 s.

[0052] S4: The raw data was converted to .mzXML format using ProteoWizard, and then peak alignment, retention time correction, and peak area extraction were performed using XCMS software. The data extracted by XCMS were first subjected to compound structure identification and data preprocessing, then experimental data quality evaluation, and finally data analysis.

[0053] 3.2 Statistical Table of Verification Results The relevant statistical data of the validation set biomarkers are shown in Tables 2 and 3. In Table 2, the mean and standard deviation are the expression levels of the test compounds. The mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The p-value is calculated using the formula of the rank-sum test. The lower the p-value, the greater the difference between the glioma group and the meningioma group, and the more accurate the detection statistical results.

[0054] Table 2 - Relevant statistical data of negative ion biomarkers in the validation set

[0055] Table 3 - Relevant expression levels of biomarkers in the validation set

[0056] In practice, compound quantification is usually relative, measured in units of relative intensity or peak area, rather than specific physical units. In metabolomics analysis, compound expression levels are typically expressed as peak areas, and these quantitative values ​​are usually relative, used to compare the relative abundance of compounds in different samples.

[0057] As shown in Tables 2 and 3, the seven positive ion salivary compounds of this invention, as detection biomarkers, exhibit high accuracy and can be used to distinguish between glioma and meningioma in patients undergoing testing (intracranial tumors), all without any invasiveness. Furthermore, the seven negative ion salivary biomarkers can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0058] Example 4: Data Analysis 4.1 OPLS-DA analysis for screening salivary biomarkers Orthogonal partial least squares discriminant analysis (OPLS-DA) revealed significant differences in the metabolic profiles between the glioma and meningioma groups. This study used a fold change log2 (FC) > 1, OPLS-DA VIP > 1, and P value < 0.05 as screening criteria for compounds with significant differences. Seven compounds with significant differences in negative ion patterns were identified in both glioma and meningioma patients: 2-(4-chloro-2-methylphenoxy)acetic acid, quillic acid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX.

[0059] 4.2 Rank-sum test The saliva markers mined from OPLS-DA analysis were then subjected to a rank-sum test, such as... Figure 2 As shown, significant differences were found in the levels of the seven compounds in the negative ion mode between the meningioma group and the meningioma group. Note: This means P < 0.05; This means P < 0.01; This represents P < 0.001. P is the significance level (p value).

[0060] 4.3 Establishment of Logistic Regression Model Using 80% of the saliva biomarkers mined from the data, a logistic regression formula for the training model was constructed using a logistic regression algorithm on seven compounds in the negative ion mode within RStudio software (R software for short): y=-3.0954353173525-4.22561095659038 10 -10 ×x1-9.00482035672691 10 -9 ×x2+6.29522397116953 10 -9 ×x3+2.05175662876307 10 -8 ×x4+2.36382458451883 10 -8 ×x5-4.34068596792601 10 -9 ×x6+5.03887612874253 10 -9 ×x7; Where y is the dominance logarithm of the patient being tested; Furthermore, the probability of health of the patients to be tested is calculated, and the formula is as follows: Z = exp(y) / {1 + exp(y)} Where Z represents the probability that the patient being tested has glioma, exp(y) is the natural exponential function of y, and x1 to x7 represent the expression levels of seven compounds: x1 represents the expression level of 2-(4-chloro-2-methylphenoxy)acetic acid; x2 represents the expression level of anthraflavic acid; x3 represents the expression level of ciprofibrate; x4 represents the expression level of Ganoderic acid A; x5 represents the expression level of lapachol; x6 represents the expression level of mesoporphyrin IX; and x7 represents the expression level of quilaic acid.

[0061] 4.3 ROC Validation Results Based on the data in Table 3 above, a receiver operating characteristic (ROC) curve analysis was performed to obtain the cutoff value (optimal cutoff value).

[0062] RStudio software was used to calculate specificity and sensitivity and to plot the ROC curve. The software first calculates the threshold of the actual measured value, and then calculates the corresponding number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN). Specificity (true negative rate) = TN / (TN+FP), and sensitivity (true positive rate) = TP / (TP+FN). The ROC curve can be constructed by subtracting specificity and sensitivity from 1. The integral of the ROC curve is AUC (Area Under Curve).

[0063] To calculate the specificity and sensitivity of an indicator, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of the indicator.

[0064] The expression levels of individual acidic biomarkers were directly analyzed using receiver operating characteristic (ROC) curves to determine the cutoff value. The ROC curve for the predicted score is shown below. Figure 3As shown in Table 4, the AUC, optimal cutoff value, sensitivity, and specificity of the mimicry markers and the prediction scoring methods for each single compound are presented.

[0065] Table 4. Results of ROC diagnostic curves in negative ion mode

[0066] In the above method, this invention newly discovered seven negative ion salivary biomarkers that are highly correlated with gliomas and meningiomas. The expression levels of each negative ion salivary biomarker are different in gliomas and meningiomas. This invention uses validation set data combined with ROC curves (… Figure 3 The study found that the above seven negative ion salivary biomarkers have high specificity and sensitivity as analytical detection variables. Therefore, the above seven negative ion salivary biomarkers can be used to distinguish between glioma patients and meningioma patients. Using one or more of these seven compounds as detection biomarkers can be used to differentiate between glioma and meningioma. This method is completely non-invasive and has high accuracy.

[0067] Further research found that, for example Figure 1 As shown, among the seven negative ion compounds mentioned above, seven compounds were present in low concentrations (or even decreasing trends) in patients with gliomas, including: 2-(4-chloro-2-methylphenoxy)acetic acid, quillic acid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX.

[0068] Depend on Figure 1 , Figure 2 As described in Tables 2 and 3, if it is difficult to diagnose or distinguish between meningioma and glioma in a patient using imaging alone, medical staff can test the patient's saliva compounds (metabolites) and diagnose the condition using the following methods: (1) If, after continuous observation over multiple time periods, the patient under test is found to have high levels (or even a significant increasing trend) of one or more of the following in their saliva: 2-(4-chloro-2-methylphenoxy)acetic acid, quillic acid, anthraflavic acid, ciprofibrate, Ganoderic acid A, Lapachol, and Mesoporphyrin IX, then the patient is more likely to have a meningioma.

[0069] (2) If, after continuous observation over multiple time periods, the seven acidic compounds in the patient's saliva do not exhibit the pattern described in (1) above, then medical staff can combine... Figure 1 Based on the content and concentration of the seven negative ion salivary markers mentioned above, a preliminary judgment is made regarding the specific disease of the patient. In practice, if medical staff want to more accurately determine whether the patient has a meningioma or a glioma, they can calculate the type of meningioma or glioma based on the relevant expression data of the seven negative ion salivary markers and referring to the technical solutions described in Examples 5 and 6 (see Example 6 for details).

[0070] It should be noted that the patient to be tested in this embodiment refers to a patient who has been preliminarily diagnosed by medical staff as having a glioma or meningioma after imaging examination. This embodiment is intended to help medical staff further distinguish and confirm whether the patient to be tested has a glioma or a meningioma, especially when the patient is unwilling to undergo invasive biopsy (invasive biopsy carries the risk of surgical trauma and neurological damage).

[0071] Example 5 Based on the above embodiments, this embodiment provides a computer program product, which is used to execute a method for determining whether a patient to be tested is a glioma patient or a meningioma patient, including the following steps: 1) Obtain the expression levels of a single negative ion salivary marker in the patient to be tested; wherein, the single negative ion salivary marker includes 2-(4-chloro-2-methylphenoxy)acetic acid, quiillaic acid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX; 2) Substitute the expression level of each individual negative ion salivary biomarker into the binary logistic regression equation to calculate the logarithm y of the patient's dominance; y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6+B7×x7; Where y is the logarithm of the dominant factor of the patient being tested; A is the intercept term; B1-B7 are the regression coefficients of the independent variables; x1-x7 are the expression levels of seven compounds, specifically: x1 is the expression level of 2-(4-chloro-2-methylphenoxy)acetic acid; x2 is the expression level of anthraflavic acid; x3 is the expression level of ciprofibrate; x4 is the expression level of Ganoderic acid A; x5 is the expression level of lapachol; x6 is the expression level of mesoporphyrin IX; and x7 is the expression level of quilaic acid.

[0072] Furthermore, through research and analysis of the data in Table 3 above, this invention determined the parameter values ​​of A, B1 to B7, specifically: A is -3.0954353173525, and B1 is -4.22561095659038. 10 -10 B2 is -9.00482035672691 10 -9 B3 is 6.29522397116953 10 -9 B4 is 2.05175662876307 10 -8 B5 is 2.36382458451883 10 -8 B6 is -4.34068596792601 10 -9 B7 is 5.03887612874253 10 -9 .

[0073] Therefore, the formula for calculating the logarithm y of the adjusted advantage is: y=-3.0954353173525-4.22561095659038 10 -10 ×x1-9.00482035672691 10 -9 ×x2+6.29522397116953 10 -9 ×x3+2.05175662876307 10 -8 ×x4+2.36382458451883 10 -8 ×x5-4.34068596792601 10 -9 ×x6+5.03887612874253 10 -9 ×x7; 3) Calculate the probability Z that the patient to be tested has glioma based on y, Z = exp(y) / {1 + exp(y)}; where exp(y) is an exponential function of y; 4) Based on the comparison between probability Z and the reference value, determine whether the patient to be tested is a glioma patient or a meningioma patient.

[0074] In practice, a Z-score greater than 0.5 indicates a higher probability that the patient has a glioma; a Z-score less than 0.5 indicates a higher probability that the patient has a meningioma; a Z-score of 0.5 indicates that the patient may have either a meningioma or a glioma, in which case further testing using other methods is necessary, such as blood tests and assessment of physical signs. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to use additional testing methods.

[0075] Example 6 Based on the product and method of Example 5, a method is provided to determine whether a patient to be tested has glioma or meningioma, the specific steps of which are as follows: 1) Obtain the expression levels of individual negative ion salivary markers in the patients to be tested; 2) Substitute the expression level of each individual negative ion salivary biomarker into the binary logistic regression equation to calculate the logarithm y of the patient's dominance. The negative ion salivary biomarkers include 2-(4-chloro-2-methylphenoxy)acetic acid, quiillaicacid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX. 3) Calculate the probability Z that the patient to be tested has glioma based on y, Z = exp(y) / {1 + exp(y)}; where exp(y) is an exponential function of y; 4) Based on the comparison between probability Z and the reference value, determine whether the patient to be tested is a glioma patient or a meningioma patient.

[0076] In practice, a Z-score greater than 0.5 indicates a higher probability that the patient has a glioma; a Z-score less than 0.5 indicates a higher probability that the patient has a meningioma; a Z-score of 0.5 indicates that the patient may have either a meningioma or a glioma, in which case further testing using other methods is necessary, such as blood tests and assessment of physical signs. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to use additional testing methods.

[0077] Example 7 Based on the description of embodiments 1 to 5 above, it can be seen that the predictive effect of the markers selected in this invention is good. Medical staff can use one or more of the seven negative ion saliva markers as detection and diagnostic criteria to determine whether the patient to be tested is a glioma patient or a meningioma patient. Medical staff can also combine the seven negative ion saliva markers together as markers to detect and diagnose the sample to determine whether the patient to be tested is a glioma patient or a meningioma patient.

[0078] Therefore, this embodiment also provides a reagent related to glioma and meningioma, which can be used in the preparation of products related to glioma and meningioma to determine whether the patient to be tested has glioma or meningioma; at the same time, the negative ion saliva marker can be selected from the seven negative ion saliva markers discovered in this invention, that is, the negative ion saliva marker in the test reagent can include one or more of the following: 2-(4-chloro-2-methylphenoxy)acetic acid, quiillaicacid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX.

[0079] Example 8 This embodiment also provides a kit that may contain the detection reagent described in Example 7 to determine whether the patient to be tested has glioma or meningioma. The limitations of the kit and the technical solutions of this invention can be found in the description of Example 7 above, and will not be repeated here. Similarly, the above kit can also be used in the preparation of products for detecting glioma, and will not be repeated here.

[0080] Example 9 The present invention also provides a product related to glioma and meningioma to determine whether a patient to be tested has glioma or meningioma; the product is specific to one or more of the seven negative ion salivary biomarkers discovered in the present invention, and the product includes primers, probes, antibodies, aptamers or chips.

[0081] As can be seen from the descriptions of Examples 1-5 and from conventional methods in the art, it should be feasible for those skilled in the art to produce corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) when using the seven newly discovered negative ion saliva markers as negative ion saliva markers, and will not be elaborated here.

[0082] Conclusion and explanation: 1. Single compound prediction and differentiation effect: combined with Table 4 and Figure 3 It can be seen that Ganoderic acid A has a better predictive and differentiating effect, while Lapachol has a slightly better predictive and differentiating effect. All seven acidic compounds mentioned above can be used to distinguish whether a patient has a glioma or a meningioma.

[0083] 2. Predictive effect of mimicry markers: combining Table 4 and Figure 3 It can be seen that the positive ion mimicry marker (7 compounds combined together) has the highest prediction and differentiation accuracy, which is over 99%, and can be used to accurately distinguish whether a patient under test has a glioma or a meningioma.

[0084] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. A negative ion salivary biomarker associated with gliomas and meningiomas, characterized in that, The negative ion salivary markers include 2-(4-chloro-2-methylphenoxy)acetic acid and / or quiillaic acid.

2. The negative ion saliva marker according to claim 1, characterized in that, The negative ion salivary markers also include one or more of anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX.

3. The negative ion saliva marker according to claim 2, characterized in that, The negative ion salivary markers include 2-(4-chloro-2-methylphenoxy)acetic acid, quillic acid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX.

4. The use of a reagent for detecting the negative ion salivary marker according to any one of claims 1 to 3 in the preparation of products related to glioma and meningioma.

5. A reagent kit, characterized in that: The reagent comprises a detection reagent for detecting the negative ion saliva markers as described in any one of claims 1-3.

6. Use of the kit of claim 5 in the preparation of products related to glioma and meningioma.

7. A product related to glioma and meningioma, characterized in that: The product includes primers, probes, antibodies, aptamers, or chips that are specific to the negative ion saliva markers of any one of claims 1 to 3.

8. A computer program product, characterized in that: The computer program product is used to perform a method for determining whether a patient under test has a glioma or a meningioma, including the following steps: To obtain the expression levels of a single negative ion salivary biomarker in the patient under test; The expression levels of each individual negative ion salivary biomarker were substituted into a binary logistic regression equation to calculate the logarithm y of the patient's dominance. The negative ion salivary biomarkers include 2-(4-chloro-2-methylphenoxy)acetic acid, quiillaic acid, anthraflavic acid, ciprofibrate, ganoderic acid A, lapachol, and mesoporphyrin IX. The probability Z of a patient being diagnosed with glioma is calculated based on y, where Z = exp(y) / {1 + exp(y)}; and exp(y) is an exponential function of y. Based on the comparison between probability Z and the reference value, it is determined whether the patient to be tested is a glioma patient or a meningioma patient.

9. The computer program product according to claim 8, characterized in that: The formula for the binary logistic regression equation is: y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6+B7×x7; Where A is the intercept term, B1 to B7 are the regression coefficients of the independent variables; x1 is the expression level of 2-(4-chloro-2-methylphenoxy)acetic acid; x2 is the expression level of anthraflavic acid; x3 is the expression level of ciprofibrate; x4 is the expression level of Ganoderic acid A; x5 is the expression level of Lapachol; x6 is the expression level of Mesoporphyrin IX; and x7 is the expression level of quilaic acid.

10. The computer program product according to claim 9, characterized in that: A is -3.0954353173525, and B1 is -4.22561095659038. 10 -10 B2 is -9.00482035672691 10 -9 B3 is 6.29522397116953 10 -9 B4 is 2.05175662876307 10 -8 B5 is 2.36382458451883 10 -8 B6 is -4.34068596792601 10 -9 B7 is 5.03887612874253 10 -9 .

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